Cosmopolitan marine bacteria facilitate a vast phytoplankton-derived sulfonate-based carbon flow through sulfoquinovosidases
Bibliographic record
Abstract
Sulfoquinovose (SQ) and sulfoquinovosyl glycerol (SQGro) are derived from abundant membrane sulfolipids termed sulfoquinovosyl diacylglycerols (SQDG) and produced by photosynthetic organisms, serving as sources of carbon and sulfur for bacteria. The conversion processes of these sulfoquinovosyl compounds within marine ecosystems, and their quantitative contributions to the marine organic matter pool, are poorly understood. Here, we identify Alteromonas macleodii, a marine bacterium capable of metabolizing SQ and SQGro through a sulfoquinovosidase. This enzyme converts SQGro to SQ and is a member of a clade within glycoside hydrolase family 31, distinct from other sulfoquinovosidases. The ubiquitous presence of sulfoquinovosidases and their transcripts throughout marine environments implicates active metabolism of sulfoquinovose glycosides, particularly in the sunlit surface ocean. We further demonstrate that marine algae produce significant quantities of cellular SQGro, and we estimate the annual turnover of SQGro using field samples from coastal and open ocean environments. Together with SQDG and SQ, these sulfoquinovosyl compounds constitute a substantial portion of the marine organic carbon turnover, estimated at around 1.5 petagrams of carbon per annum. These findings reveal a vast, previously unappreciated pool of organosulfonates within the microbial food web that contributes significantly to the marine carbon and sulfur cycles. Sulfoquinovosyl diacylglycerols are produced by photosynthetic organisms and serve as sources of carbon and sulfur for bacteria. This work characterizes a sulfoquinovosidase, unveils a reservoir of organosulfur compounds in the ocean and elucidates their critical role in marine carbon cycling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".